基于肿瘤全域表观扩散系数纹理分析预测上皮性卵巢癌复发的研究

doi:10.12117/jccmi.2020.01.013

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Abstract Objective: To investigate the recurrence prediction value of apparent diffusion coefficient(ADC) based on whole tumor volume measurement in patients with epithelial ovarian cancer(EOC). Methods: A retrospective analysis of 49 patients with pathologically proven EOC who underwent preoperative DWI imaging(b=0, 800 s/mm2) was carried out. The post-processing software was used to map the region of interest of the whole tumor on the ADC map, and the texture parameters such as skewness, kurtosis, entropy, inertia, correlation, contrast, variance were extracted and analyzed. Multivariate Logistic regression analysis was used to determine the best predictor of recurrence, and the predictive efficiency of the relevant parameters for recurrence after surgery was evaluated by receiver operating characteristic curve. Results: Tumor size and ascites were higher in recurrence group than those in non-recurrent group. The differences were statistically significant(P<0.05). The difference between the two groups in FIGO staging was statistically significant. The parameters of texture analysis, including inertia, contrast, variance and entropy in recurrence group were higher than those in non-recurrent group. Kurtosis, quantile 10, quantile 25 and correlation in recurrence group were lower than those in non-recurrent group. The differences were statistically significant(P<0.05). ROC curve analysis showed that the area under the curve of kurtosis, inertia, correlation, tumor size and FIGO stage combined to prediction recurrence was the highest, 0.929. Conclusion: The preoperative texture analysis of ADC images based on total tumor volume helps predicting recurrence of EOC.

Key words： Ovarian neoplasms Magnetic resonance imaging

Received: 24 January 2019

PACS:	Ｒ737.31
	Ｒ445.2

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	MAO Mi-mi
	FENG Feng

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MAO Mi-mi,FENG Feng. Prediction recurrence value of apparent diffusion coefficient based on whole tumor volume measurement in #br# patients with epithelial ovarian cancer[J]. JOURNAL OF CHINA MEDICAL IMAGING, 2020, 31(1): 52-56.

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http://www.jccmi.com.cn/EN/10.12117/jccmi.2020.01.013 OR http://www.jccmi.com.cn/EN/Y2020/V31/I1/52

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